The present invention relates to an intelligent transportation system, method, and computer program product in signalized traffic systems, and more specifically, to a diagnosis tool for an adaptive signalized control system.
Signalized traffic systems are a fundamental section of an intelligent transportation system because they contribute to the management of congestion on an urban level. Signalized traffic systems are currently used in 154 cities in over 27 countries.
Conventionally, signalized traffic systems mostly refer to systems and methods for detecting traffic congestion. For example, some systems relate to integrating traffic, weather, incident, pavement condition, and roadway operational data to model and estimate traffic states for generating information for consumer and commercial utility. Accurate routing information for particular roadway segments are produced and the system provides a consumer with accurate, real-time traffic routing information integrating traffic, weather and congestion data.
However, the aforementioned signalized traffic systems main goal is to provide rerouting given the integration of all the inputs. The signalized traffic system requires a plurality of inputs in order to provide any meaningful output. This conventional system exhibits deficiencies by merely providing rerouting without any type of diagnosis of the event which caused the need to reroute.
Other signalized traffic systems relate generally to modeling traffic movement of a region, by running a traffic simulation based on at least one sensor model generated by selecting a subset of at least one traffic sensor. These systems use an information technology driven approach. Such techniques also increase supply side (roads, vehicles, etc.) and demand side (commuting needs) efficiency to overcome demand-supply mismatches, and make roads safer.
The above system takes input data from multiple sources and finds the optimal combination of different sensor types in order to satisfy a cost-benefit goal (in terms of accuracy, cost and coverage) for different traffic patterns. The system does not provide identification problems in signalized traffic control systems. Also, the proposed system requires the inputs from multiple data sources and requires a selection of a subset of the plurality of sensors.
Other systems utilize real-world data collected from transportation networks to incorporate the data's intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. For example, systems use the spatio-temporal behaviors of rush hours and events for better prediction by taking historical rush-hour behavior into account to improve the accuracy of traditional predictors.
This conventional system seeks to improve prediction accuracy of a basic time series model (ARIMA) with a hybrid approach to include the impact of incidents directly detected from sensors in real time to enhance predictions. However, the ARIMA model is used for a regression problem, and therefore, the proposed system cannot predict discrete events, for example, congestion propagation. That is, conventional systems merely identify congestion of the systems but fail to identify problems within the systems and how the problems throughout the systems.
More recently, there has been proposed signalized traffic systems relating to a ground transportation network matching individuals with transportation capacity on a supply and demand basis. The systems utilize an active monitoring system for generation of traffic flow data; combined with a central information repository, to provide real time network for traffic flow throughout a metropolitan area along with enabling any of the Transport Capacity vehicles to act as “traffic probes” reporting on throughput and delays in traffic. Other recent systems have proposed a traffic information gathering system using cellular phone networks for automated intelligent traffic signal control where location information is obtained and continuously updated from vehicle-based cellular phones. The system processes the information and uses the information as an input to Real Time Urban Traffic Guidance for Vehicular Congestion and Intelligent Traffic Control Systems.